code: def padSeqs(
      sequences,
      maxlen=None,
      truncated=False,
      pad_method="post",
      trunc_method="pre",
      dtype="int32",
      value=0.0,
  ):
      if not hasattr(sequences, "__len__"):
          raise ValueError("`sequences` must be iterable.")
      lengths = []
      for x in sequences:
          if not hasattr(x, "__len__"):
              raise ValueError(
                  "`sequences` must be a list of iterables. "
                  "Found non-iterable: " + str(x)
              )
          lengths.append(len(x))
  
      num_samples = len(sequences)
      seq_maxlen = np.max(lengths)
  
      if maxlen is not None and truncated:
          maxlen = min(seq_maxlen, maxlen)
      else:
          maxlen = seq_maxlen
      sample_shape = tuple()
      for s in sequences:
          if len(s) > 0:
              sample_shape = np.asarray(s).shape[1:]
              break
  
      x = (
          np.ones((num_samples, maxlen) + sample_shape) * value
      ).astype(dtype)
      for idx, s in enumerate(sequences):
          if not len(s):
              print("empty list/array was found")
              continue
          if trunc_method == "pre":
              trunc = s[-maxlen:]
          elif trunc_method == "post":
              trunc = s[:maxlen]
          else:
              raise ValueError(
                  'Truncating type "%s" not understood' % trunc_method
              )
  
          trunc = np.asarray(trunc, dtype=dtype)
          if trunc.shape[1:] != sample_shape:
              raise ValueError(
                  "Shape of sample %s of sequence at position %s is different from expected shape %s"
                  % (trunc.shape[1:], idx, sample_shape)
              )
  
          if pad_method == "post":
              x[idx, : len(trunc)] = trunc
          elif pad_method == "pre":
              x[idx, -len(trunc) :] = trunc
          else:
              raise ValueError(
                  'Padding type "%s" not understood' % pad_method
              )
      return x
  
imports: import logging
  import json
  import numpy
  from collections import OrderedDict
  from .None import ontology
label_desc: Use image node_id 5 for calling a global function with example usage: padSeqs(sequences, maxlen, truncated, pad_method, trunc_method, dtype, value) and returns: x